Is my food safe? - AI-based classification of lentil flour samples with trace levels of gluten or nuts.
Adulteration
Gluten
Image analysis
Lentils
Nuts
ResNet34
Journal
Food chemistry
ISSN: 1873-7072
Titre abrégé: Food Chem
Pays: England
ID NLM: 7702639
Informations de publication
Date de publication:
30 Aug 2022
30 Aug 2022
Historique:
received:
26
01
2022
accepted:
25
03
2022
pubmed:
3
4
2022
medline:
6
5
2022
entrez:
2
4
2022
Statut:
ppublish
Résumé
An artificial intelligence-based method to rapidly detect adulterated lentil flour in real time is presented. Mathematical models based on convolutional neural networks and transfer learning (viz., ResNet34) have been trained to identify lentil flour samples that contain trace levels of wheat (gluten) or pistachios (nuts), aiding two relevant populations (people with celiac disease and with nut allergies, respectively). The technique is based on the analysis of photographs taken by a simple reflex camera and further classification into groups assigned to adulterant type and amount (up to 50 ppm). Two different algorithms were trained, one per adulterant, using a total of 2200 images for each neural network. Using blind sets of data (10% of the collected images; initially and randomly separated) to evaluate the performance of the models led to strong performances, as 99.1% of lentil flour samples containing ground pistachio were correctly classified, while 96.4% accuracy was reached to classify the samples containing wheat flour.
Identifiants
pubmed: 35366636
pii: S0308-8146(22)00794-4
doi: 10.1016/j.foodchem.2022.132832
pii:
doi:
Substances chimiques
Glutens
8002-80-0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
132832Informations de copyright
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